Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO

TL;DR

This paper introduces a two-stage deep learning approach for uplink channel estimation in one-bit massive MIMO systems, significantly improving accuracy with fewer pilots by combining supervised and unsupervised models.

Contribution

It presents a novel two-stage deep learning pipeline that effectively compensates for quantization loss and denoises, outperforming existing estimators in one-bit massive MIMO.

Findings

Achieves 5-10 dB gain in channel estimation error.

Requires about 20 pilots per coherence interval.

Outperforms state-of-the-art estimators.

Abstract

We develop a two-stage deep learning pipeline architecture to estimate the uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is composed of two separate generative deep learning models. The first one is a supervised learning model and designed to compensate for the quantization loss. The second one is an unsupervised learning model and optimized for denoising. Our results show that the proposed deep learning-based channel estimator can significantly outperform other state-of-the-art channel estimators for one-bit quantized massive MIMO systems. In particular, our design provides 5-10 dB gain in channel estimation error. Furthermore, it requires a reasonable amount of pilots, on the order of 20 per coherence time interval.